Processing Data Using Apache Hive

With the increased adoption of data based information systems, there is a great need for an information system that allows users to query the data sets and get feedback within the shortest time possible. Distributed computing is one of the areas where there is the need for quick data processing to enable users to get quick responses.

The Hadoop framework has proven to be the best framework for handling large datasets in an environment where distributed computing is used. The success of Hadoop in delivering results in the distributed computing environment has been enabled through the use of Apache Hive.

Using the Apache Hive, then the Hadoop framework will ensure the users in a distributed computing environment to get the best results in a timely manner and the best quality results. So what is Apache Hive and how do you use it to process data?

Apache Hive Defined

This is a data set warehouse that works as an open source system to enable users querying the data set by enabling analyzing of large datasets that are stored in the Hadoop file system. Introduced in 2008, the Apache Hive has continued to be the preferred solution when it comes to the creation of interactive database queries in the Hadoop based data system.

Before analyzing how the Apache Hive works, it is important to note that Hadoop is used as a system for organizing and storing massive data sets of different formats assuming a structured and unstructured format from a wide range of sources. To effectively function, this is where the Apache Hive becomes an important tool. There are several advantages attached to this. So how does Apache Hive Work?

Understanding How to Process Data Using Apache Hive

Step One: Executing a Query

You will start by executing your request using either a command line or the GUI. The web-based GUI will send the request to the driver used in the database. Remember that different databases use different drivers.

Step Two: Driver Will Work out A Plan to Respond

With the help of the query compiler, the driver will plan on the best format to respond to the query you have submitted.

Step Three: Metadata Request

The compiler will ask for metadata request from the metastore. Once the metastore responds with metadata, the compiler checks the response and communicates to the driver for next action.

Step Four: Execute the Plan

The execution plan is sent to the server engine for action. The engine will execute the Metadata operations in conjunction with the metastore.

Step Five: Fetch and Send the Results

Once the data nodes sent the results, then it is ready to be sent to the driver so that driver can now respond to Apache Hive. The Apache hive receives the response and displays for the user to form a conclusion.

That is how you will process the data when using Apache Hive. The process is so simple, and so the results are timely. Using this platform, you will be able to query data with a SQL based language and secondly, it works with traditional data integration and analytical tools.